Bryan Hong

1/ 🚨 New preprint

Key Moments Scaffold the Semantic Structure of Narratives

Using spoken recall and annotations from three naturalistic datasets with topic modeling, we ask: which parts of a narrative contribute most to its semantic structure and subsequently memory?

Preprint: osf.io/dcfvw

🚨 New paper! Think of a place you feel deeply attached to. When you're in that place, do you feel like your life is more meaningful? New work from @ashleykrause.bsky.social suggests you probably do!

Just accepted at Journal of Environmental Psychology, we find places can give our life meaning 🧵

Are episodic and semantic memory really that different? Using closely matched tasks, we found no substantial neural differences between recalling personal experiences and general knowledge: https://www.nature.com/articles/s41562-025-02390-4

How do we balance external attention to the outside world and internal attention to our thoughts & memories?

We review evidence that external and internal attention can compete, unfold concurrently, or cooperate!

Loved working on this with @samversc.bsky.social & @tobiasegner.bsky.social!

What if we could tell you how well you’ll remember your next visit to your local coffee shop? ☕️

In our new Nature Human Behaviour paper, we show that the 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗮 𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 can be measured with neuroimaging – and 𝘁𝗵𝗮𝘁 𝘀𝗰𝗼𝗿𝗲 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝘀 𝗵𝗼𝘄 𝘄𝗲𝗹𝗹 𝗻𝗲𝘄 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 𝘄𝗶𝗹𝗹 𝘀𝘁𝗶𝗰𝗸.

Abstract When we empathize with someone going through something, we often draw on our past experiences with the someone and the something. These kinds of experiences ground "thick empathy", a form of empathy that has been largely overlooked in the psychology and neuroscience literature. Consider how a mother, empathizing with her daughter about to give birth, can draw on her own experience of childbirth, and her relationship with her daughter, to deeply grasp what her daughter is going through in a way that others who lack those experiences cannot. I argue that thick empathy deserves more empirical attention because it is associated with well-being and helps us build networks of effective mutual social support. My analysis highlights novel risks and dilemmas posed by "empathy machines" that promise to enhance or even replace human empathy and are becoming increasingly popular as a potential solution to widespread loneliness. Even when empathy machines provide value to individuals, their widespread adoption risks imposing collective emotional and epistemic costs that ultimately make it harder for us to empathize well. Keywords: empathy, understanding, experience, thick description, ethnography, phenomenal knowledge, interpersonal knowledge, virtual reality, artificial intelligence, chatbots

New preprint: Empathy, Thick and Thin
papers.ssrn.com/sol3/papers....

It is perhaps foolhardy to attempt to say something new about a topic as widely studied as empathy. I tried anyway! 1/

Excited to announce that I'll be officially starting a new role as an Assistant Professor at the University of Guelph as of today—looking forward to working with all the amazing students, faculty, and members of the Guelph community!! ✨

The Science of Visual Data Communication: What Works - Steven L. Franconeri, Lace M. Padilla, Priti Shah, Jeffrey M. Zacks, Jessica Hullman, 2021 Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, an...

I'm surprised I only came across it now, but this review on improving communication in data visualization is excellent.
journals.sagepub.com/doi/10.1177/...

We strongly suggest using the following labels: praise: Praises highlight something positive. Try to leave at least one of these comments per review. Do not leave false praise (which can actually be damaging). Do look for something to sincerely praise. nitpick: Nitpicks are trivial preference-based requests. These should be non-blocking by nature. suggestion: Suggestions propose improvements to the current subject. It’s important to be explicit and clear on what is being suggested and why it is an improvement. Consider using patches and the blocking or non-blocking decorations to further communicate your intent. issue: Issues highlight specific problems with the subject under review. These problems can be user-facing or behind the scenes. It is strongly recommended to pair this comment with a suggestion. If you are not sure if a problem exists or not, consider leaving a question. todo: TODO’s are small, trivial, but necessary changes. Distinguishing todo comments from issues: or suggestions: helps direct the reader’s attention to comments requiring more involvement. question: Questions are appropriate if you have a potential concern but are not quite sure if it’s relevant or not. Asking the author for clarification or investigation can lead to a quick resolution. thought: Thoughts represent an idea that popped up from reviewing. These comments are non-blocking by nature, but they are extremely valuable and can lead to more focused initiatives and mentoring opportunities. chore: Chores are simple tasks that must be done before the subject can be “officially” accepted. Usually, these comments reference some common process. Try to leave a link to the process description so that the reader knows how to resolve the chore. note: Notes are always non-blocking and simply highlight something the reader should take note of.

I recently discovered Conventional Comments (conventionalcomments.org) for providing a pseudo-standard set of labels for feedback and just tried it for an article review and it was really helpful to specify issues vs. thoughts vs. suggestions, etc. Hopefully it's helpful for the authors too!

Thrilled to announce a new paper out this weekend in
@cognitionjournal.bsky.social.

Moral psychologists almost always use self-report scales to study moral judgment. But there's a problem: the meaning of these scales is inherently relative.

A 2 min demo (and a short thread):

1/7

A memory can be represented at different levels of granularity, from highly specific to generalized.

Different representational formats of a memory can be used at different times or in different contexts, and draw on different neural representations.

doi.org/10.31234/osf...

An arrow with a LaTeX equation

Trigonometric functions and a unit circle

A bivariate change model with structured residuals

A hierarchical model of cognitive abilities

Now on CRAN, ggdiagram is a #ggplot2 extension that draws diagrams programmatically in #Rstats. Allows for precise control in how objects, labels, and equations are placed in relation to each other.
wjschne.github.io/ggdiagram/ar...

On the left: an illustration from Brooke's 1904 rendition of Goldilocks and the Three Bears, where Little bear discovers their favourite chair is broken 😲. On the right, a sketch of what a corresponding "situation model" might contain.

How might stories shed light on brain function? Check out this opinion piece by @alexbarnett.bsky.social and I about the DMN and "situation models" -- our understanding of the current "state of affairs" in a story (or even experience).

www.sciencedirect.com/science/arti...

Hippocampal mismatch signals are based on episodic memories and not schematic knowledge | PNAS Prediction errors drive learning by signaling mismatches between expectations and reality, but the neural systems supporting these computations rem...

We make predictions based on general knowledge and/or specific memories. Different brain areas are active when these distinct predictions are violated – and hippocampus selectively responds to prediction errors based on episodic memory.

Cool work by @chrismbird.bsky.social @ayab.bsky.social et al!

<em>Child Development</em> | SRCD Journal | Wiley Online Library Cognitive development is associated with how predictable caregivers are, but the mechanisms driving this are unclear. One possibility is caregiver predictability initially shapes how infants gather i...

Happy to share "The Dynamics of Caregiver Unpredictability Shape Moment-to-Moment Infant Looking During Dyadic Interaction," out now in Child Development thanks to a large team of people I worked on this with! srcd.onlinelibrary.wiley.com/doi/pdfdirec...

📣 New preprint from the SCIMaP team!

Across three studies, we show that communicating the economic impact of NIH funding cuts—especially with interactive quizzes and maps—decreases approval and motivates action to oppose the cuts, across the political spectrum. 🧵 1/8
osf.io/preprints/ps...

New paper with @mujianing.bsky.social & @prestonlab.bsky.social! We propose a simple model for human memory of narratives: we uniformly sample incoming information at a constant rate. This explains behavioral data much better than variable-rate sampling triggered by event segmentation or surprisal.

Comment: Rethinking behavior change interventions in policymaking

📢 New commentary out today in Nature Human Behaviour!
We argue that behavior change interventions often suffer from a one-sided success focus. But failures may reveal structural barriers people face.

🔗 rdcu.be/ex8hR

#BehavioralScience #PublicPolicy

Perceived community alignment increases information sharing - Nature Communications Information sharing is a ubiquitous and consequential behavior. Here, the authors use neuroimaging and behavioral studies to show that people are driven to share information that they believe will be ...

Excited to ✨share✨ that our paper on ✨sharing✨ is published! Across 3 studies that build on one another, we show that perceived alignment with one's peers increases the likelihood of information sharing.

www.nature.com/articles/s41...

Experimentology cover: title and curves for distributions.

Experimentology is out today!!! A group of us wrote a free online textbook for experimental methods, available at experimentology.io - the idea was to integrate open science into all aspects of the experimental workflow from planning to design, analysis, and writing.

The self-reference memory bias is preceded by an other-reference bias in infancy - Nature Communications A classic feature of human memory is that we remember information better when it refers to ourselves. Here, the authors show that before the emergence of self-concept, infants instead remember informa...

Sharing our new paper published today in Nature Communications. In my view, this is our clearest demonstration to date that something profoundly changes in how infants encode the world around them before and after the emergence of self-representation. www.nature.com/articles/s41...

Proud to share the first preprint of my PhD w/ @barense.bsky.social & Mursal Jahed:

“Putting the testing effect to the test in the wild: Retrieval enhances real-world memories and promotes their semantic integration while preserving episodic integrity”

See thread! 🧵 osf.io/preprints/ps...

As federal funders desert mentorship programs for marginalized students, trainee-led initiatives fill the gap Grassroots organizations, led by graduate students and postdoctoral researchers, are stepping up to provide neuroscience career training and guidance for students from marginalized backgrounds—and…

In our first “Postdoc perspectives” essay, @fleabrained.bsky.social and @maribel-patino.bsky.social explain how grassroots organizations led by trainees are stepping up to provide career and mentorship guidance for neuroscience students from marginalized backgrounds.

#neuroskyence

bit.ly/4csFx0B

CNS 2025 presentations from the Barense lab

If you're at #CNS2025 come check out our lab's Sunday line-up of posters from @catalinayang.bsky.social, @bryanhong.bsky.social, @nellymatorina.bsky.social, and @laurenhomann.bsky.social.

Are you doing EMA research and wonder how to go about it? In recent work we've adressed some open questions and challenges, here is a brief summary of papers and materials.

🧵 #PsychSciSky 🧪 #StatsSky

Thrilled to see this paper out in @naturehumbehav.bsky.social after years of work by Drs. @diamondn.bsky.social and @stefsimpson.bsky.social, with Drs. Stuart Fogel, Daniel Baena, and Brian J Murray!

@baycrestfoundation.bsky.social

Grateful for the chance to share my thoughts on responsible use of LLMs in psychology research @spspnews.bsky.social #spsp2025. Here's a summary of my presentation for those who missed it. Thanks to @ashwinia.bsky.social for organizing this panel!

How should LLMs be used in psychology research? 🧵

“I am in here!” It's a sentiment I've seen expressed time and time again from non-speaking autistic individuals who were thought to be unable to express their thoughts - but ultimately gained access to communication. Hearing this call, my research is expanding in some new directions.

Schemas, reinforcement learning and the medial prefrontal cortex - Nature Reviews Neuroscience A computational account of how schemas are learned through experience is lacking. In this Perspective, Bein and Niv synthesize schema theory and reinforcement learning research to derive computational...

Excited to share this perspective with @yaelniv.bsky.social about how schemas might be learned and instantiated via reinforcement learning, latent cause inference, and dimensionality reduction, and what's the medial prefrontal cortex might be doing for all of these www.nature.com/articles/s41...

Chinatown Memory Collectors - Spacing Toronto A shiny sword, a stack of red packets, old Chinese books and brochures, a wooden folding fan, a souvenir mug, and faded photos of seniors – all are carefully displayed on a large round table draped in...

A exhibition at Waterloo architecture collects memories and mementos of Toronto's Chinatown, along with scale models of restaurants, to reimagine community heritage through storytelling. spacing.ca/toronto/2025...